Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

Cömert, Zafer; Kocamaz, Adnan Fatih

doi:10.12693/aphyspola.132.451

Cited by 78 publications

(39 citation statements)

References 23 publications

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“…He didn't apply simulation for his results or provide hybrid models for improving classification accuracy rate. Z. Cömert [12] presented the comparative metrics of five www.ijacsa.thesai.org machine learning techniques such as Artificial Neural Network (ANN) [1, 2, and 13], support vector machine [14], extreme learning machine [15], radial basis function network [16] and random forest [17]. He found that ANN technique is the most efficient in the sensitivity and specificity measures.…”

Section: Introductionmentioning

confidence: 99%

Classifying Cardiotocography Data based on Rough Neural Network

Amin¹,

Gamal²,

Salama³

et al. 2019

IJACSA

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Cardiotocography is a medical device that monitors fetal heart rate and the uterine contraction during the period of pregnancy. It is used to diagnose and classify a fetus state by doctors who have challenges of uncertainty in data. The Rough Neural Network is one of the most common data mining techniques to classify medical data, as it is a good solution for the uncertainty challenge. This paper provides a simulation of Rough Neural Network in classifying cardiotocography dataset. The paper measures the accuracy rate and consumed time during the classification process. WEKA tool is used to analyse cardiotocography data with different algorithms (neural network, decision table, bagging, the nearest neighbour, decision stump and least square support vector machine algorithm). The comparison shows that the accuracy rates and time consumption of the proposed model are feasible and efficient.

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Section: Introductionmentioning

confidence: 99%

Classifying Cardiotocography Data based on Rough Neural Network

Amin¹,

Gamal²,

Salama³

et al. 2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…In the text preprocessing, using “Word to Vector” as “ n -gram” algorithm and taking advantage of Zemberek library increased rates of success to find word roots. We use the evaluations metrics which are precision, sensitivity, F-measure, and accuracy [ 55 , 56 ]. These metrics are depending on TP (true-positive) and FP (false-positive) ratios:

…”

Section: Resultsmentioning

confidence: 99%

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

Kırelli

Arslankaya

2020

Computational Intelligence and Neuroscience

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As the usage of social media has increased, the size of shared data has instantly surged and this has been an important source of research for environmental issues as it has been with popular topics. Sentiment analysis has been used to determine people's sensitivity and behavior in environmental issues. However, the analysis of Turkish texts has not been investigated much in literature. In this article, sentiment analysis of Turkish tweets about global warming and climate change is determined by machine learning methods. In this regard, by using algorithms that are determined by supervised methods (linear classifiers and probabilistic classifiers) with trained thirty thousand randomly selected Turkish tweets, sentiment intensity (positive, negative, and neutral) has been detected and algorithm performance ratios have been compared. This study also provides benchmarking results for future sentiment analysis studies on Turkish texts.

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“…The evaluation result is an assessment using a formula by comparing the portion of data that is correctly classified and the portion of data that is misclassified [18]. Table 2 showed the evaluation result using accuracy, precision, and recall.…”

Section: Evaluation Resultsmentioning

confidence: 99%

A Feature-Driven Decision Support System for Heart Disease Prediction Based on Fisher's Discriminant Ratio and Backpropagation Algorithm

Yudianto

Fahrudin

Nugroho

2020

LKJITI

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Coronary heart disease included a group of cardiovascular, and it is a leading cause of death in low and middle-income countries. Risk factors for coronary heart disease are divided into two, namely primary and secondary risk factors. The need to identify characteristics or risk factors in heart disease patients by making the classification model. The modeling of heart disease classification to know how the system can able to reach the best prediction accuracy. Fisher's Discriminant Ratio is one of the methods for feature selection, which is used to get high discriminant features. While Backpropagation is one of the classification models to recognize patterns in heart disease patients. The experiment results showed that the accuracy of the classification model using 13 original features reached 92%. By reducing the features based on the score of the feature selection, then the lowest feature was removed from original features and left there were 12 features involved in the classification model which the accuracy increased to 93%. Furthermore, the results of determining the threshold (accuracy does not decrease continuously) and consider the effect of eliminating the lowest features that are considered quite fluctuating on accuracy. The accuracy reached 90% by eliminating the five lowest features and left eight existing features.

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Comparison of Machine Learning Techniques for Fetal Heart Rate Classification

Cited by 78 publications

References 23 publications

Classifying Cardiotocography Data based on Rough Neural Network

Classifying Cardiotocography Data based on Rough Neural Network

Sentiment Analysis of Shared Tweets on Global Warming on Twitter with Data Mining Methods: A Case Study on Turkish Language

A Feature-Driven Decision Support System for Heart Disease Prediction Based on Fisher's Discriminant Ratio and Backpropagation Algorithm

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